Collective matrix factorization (a.k.a. multiview or multiway factorization, Singh, Gordon, (2008) <doi:10.1145/1401890.1401969>) tries to approximate a matrix 'X' as the product of two lowdimensional matrices aided with secondary information matrices about rows and/or columns of 'X' which are also factorized using the same latent components. The intended usage is for recommender systems, dimensionality reduction, and missing value imputation. Implements extensions of the original model (Cortes, (2018) <arXiv:1809.00366>) and can produce different factorizations such as the weighted 'implicitfeedback' model (Hu, Koren, Volinsky, (2008) <doi:10.1109/ICDM.2008.22>), the 'weightedlambdaregularization' model, (Zhou, Wilkinson, Schreiber, Pan, (2008) <doi:10.1007/9783540688808_32>), or the enhanced model with 'implicit features' (Rendle, Zhang, Koren, (2019) <arXiv:1905.01395>), with or without side information. Can use gradientbased procedures or alternatingleast squares procedures (Koren, Bell, Volinsky, (2009) <doi:10.1109/MC.2009.263>), with either a Cholesky solver, a faster conjugate gradient solver (Takacs, Pilaszy, Tikk, (2011) <doi:10.1145/2043932.2043987>), or a nonnegative coordinate descent solver (Franc, Hlavac, Navara, (2005) <doi:10.1007/11556121_50>), providing efficient methods for sparse and dense data, and mixtures thereof. Supports L1 and L2 regularization in the main models, offers alternative mostpopular and contentbased models, and implements functionality for coldstart recommendations and imputation of 2D data.
Package details 


Author  David Cortes [aut, cre, cph], Jorge Nocedal [cph] (Copyright holder of included LBFGS library), Naoaki Okazaki [cph] (Copyright holder of included LBFGS library), David Blackman [cph] (Copyright holder of original Xoshiro code), Sebastiano Vigna [cph] (Copyright holder of original Xoshiro code) 
Maintainer  David Cortes <david.cortes.rivera@gmail.com> 
License  MIT + file LICENSE 
Version  3.2.2 
URL  https://github.com/davidcortes/cmfrec 
Package repository  View on CRAN 
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